Locally hosted LLM: hardware, models, and deployment

By 2025, running locally hosted large language models (LLMs) has transitioned from a novel project to a viable infrastructure choice, driven by advances in hardware efficiency and model performance, such as Meta's Llama 3.1 and Mistral's models. The decision to use local LLMs over cloud-based solutions hinges on data sensitivity, latency requirements, and cost, particularly at high query volumes. A survey by Andreessen Horowitz indicates a significant increase in enterprises opting for on-premise LLMs, rising from 11% in 2023 to 34% in 2025, primarily due to privacy concerns and compliance with regulations like HIPAA and GDPR. Locally hosted LLMs offer advantages in scenarios where data privacy is paramount, high query volumes make cloud costs prohibitive, low latency is necessary for real-time applications, or when custom fine-tuning is involved. The development of deployment tools like Ollama and llama.cpp has reduced setup times to mere hours, making local deployment more accessible and attractive for specific use cases.

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